Keyword: digital literacy

ABSTRACT: Generative artificial intelligence (AI) is rapidly transforming important areas of human activity, yet adults’ acceptance of generative AI applications remains insufficiently understood. This cross-sectional study examined the acceptance of generative AI applications among 460 adults recruited through online convenience sampling. Data were collected using the generative artificial intelligence acceptance scale (GAIAS), a validated instrument based on dimensions derived from the unified theory of acceptance and use of technology (UTAUT). The scale comprised four dimensions: performance expectancy, effort expectancy, facilitating conditions, and social influence. Participants reported moderate to high overall acceptance of generative AI. Effort expectancy, reflecting perceived ease of use (PEOU), received the highest mean score, followed by performance expectancy, reflecting perceived usefulness (PU), whereas social influence received the lowest mean score. This descriptive pattern indicates that perceived usability and usefulness were rated more positively than perceived social influence within the present sample. After Holm-Bonferroni correction across the 25 primary comparisons, statistically significant group differences remained for age and educational level in overall generative AI acceptance, performance expectancy, and effort expectancy. No multiplicity-adjusted differences were identified according to gender, personal computer ownership, or previous computer training. The age- and education-related findings should not be interpreted as evidence of linear relationships, independent effects, or causation. Within this digitally connected convenience sample, the findings indicate that PEOU and PU were more prominent dimensions of generative AI acceptance than social influence. Age- and education-related group differences were also observed. However, the cross-sectional design, online convenience sampling, and marked subgroup imbalances limit causal interpretation and generalizability to the wider adult population. The findings may nevertheless inform the development of accessible generative AI tools and targeted digital-literacy initiatives.

ABSTRACT: This study examines the relationship between postgraduate students’ technology skills and their perceptions of research, utilizing two psychometrically validated tools: the Technology Skills Assessment Tool (TSAT) and the Research Perception Scale (RPS). The research was conducted among 117 Master of Arts MA and M.Ed. students across government and private higher education institutions in India. The study employed a purely quantitative design using random sampling. Results from reliability analysis and exploratory factor analysis confirmed the internal consistency and construct validity of both instruments. Descriptive statistics indicated a moderate to high level of technology proficiency and positive research perception among students. Pearson’s correlation revealed significant associations between key technology domains (e.g., digital literacy, data analysis) and research confidence, motivation, and aspirations. Multiple regression analysis further established that digital skills significantly predicted students’ perceptions across different dimensions of research engagement. Additionally, no significant differences were observed across gender, program, or institution type. These findings highlight the relevance of embedding digital skill development into postgraduate curricula to strengthen research competence. The study concludes with implications for curriculum planners, teacher educators, and researchers, recommending the adoption of digitally enriched, practice-oriented research training in higher education.